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dc.contributor.authorAskarian, Mahdieh
dc.contributor.authorBenítez Iglesias, Raúl
dc.contributor.authorGraells Sobré, Moisès
dc.contributor.authorZarghami, Reza
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Química
dc.date.accessioned2016-11-28T14:34:02Z
dc.date.available2016-11-28T14:34:02Z
dc.date.issued2016-06-23
dc.identifier.citationAskarian, M., Benitez, R., Graells, M., Zarghami, R. Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels. "Expert systems with applications", 23 Juny 2016, vol. 63, p. 35-48.
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/2117/97335
dc.description.abstractDeveloping data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problem
dc.format.extent14 p.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria química
dc.subject.lcshChemical processes
dc.subject.otherMislabeling
dc.subject.otherLabel noise
dc.subject.otherUnderlying states
dc.subject.otherOperational intelligence
dc.subject.otherInteractive learning
dc.titleData-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels
dc.typeArticle
dc.subject.lemacProcessos químics
dc.contributor.groupUniversitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics
dc.contributor.groupUniversitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering
dc.identifier.doi10.1016/j.eswa.2016.06.040
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0957417416303219
dc.rights.accessOpen Access
local.identifier.drac18773409
dc.description.versionPostprint (author's final draft)
local.citation.authorAskarian, M.; Benitez, R.; Graells, M.; Zarghami, R.
local.citation.publicationNameExpert systems with applications
local.citation.volume63
local.citation.startingPage35
local.citation.endingPage48


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